Data Viz

Partitions

The Partition type summarizes sections of a data stream using any OnlineStat, and is therefore extremely useful in visualizing huge datasets, as summaries are plotted rather than every single observation.

Continuous Data

y = cumsum(randn(10^6)) + 100randn(10^6)

o = Partition(KHist(10))

fit!(o, y)

plot(o)

o = Partition(Series(Mean(), Extrema()))

fit!(o, y)

plot(o)

Categorical Data

y = rand(["a", "a", "b", "c"], 10^6)

o = Partition(CountMap(String), 75)

fit!(o, y)

plot(o)

Indexed Partitions

The Partition type can only track the number of observations in the x-axis. If you wish to plot one variable against another, you can use an IndexedPartition.

x = randn(10^6)
y = x + randn(10^6)

o = fit!(IndexedPartition(Float64, KHist(40), 40), zip(x, y))

plot(o)

x = rand(10^6)
y = rand(1:5, 10^6)

o = fit!(IndexedPartition(Float64, CountMap(Int)), zip(x,y))

plot(o, xlab = "X", ylab = "Y")

using Dates

x = rand(Date(2019):Day(1):Date(2020), 10^6)
y = Dates.value.(x) .+ 30randn(10^6)

o = fit!(IndexedPartition(Date, KHist(20)), zip(x,y))

plot(o)

K-Indexed Partitions

A KIndexedPartition is simlar to an IndexedPartition, but uses a different method of binning the x variable (centroids vs. intervals), similar to that of KHist.

For the sake of performance, you must provide a function that creates the OnlineStat you wish to calculate for the y variable.

x = randn(10^6)
y = x + randn(10^6)

o = fit!(KIndexedPartition(Float64, () -> KHist(20)), zip(x, y))

plot(o)

Histograms

s = fit!(Series(KHist(25), Hist(-5:.2:5), ExpandingHist(100)), randn(10^6))
plot(s, link = :x, label = ["KHist" "Hist" "ExpandingHist"])

Approximate CDF

o = fit!(OrderStats(1000), randn(10^6))

plot(o)

Mosaic Plots

The Mosaic type allows you to plot the relationship between two categorical variables. It is typically more useful than a bar plot, as class probabilities are given by the horizontal widths.

using RDatasets
t = dataset("ggplot2", "diamonds")

o = Mosaic(eltype(t.Cut), eltype(t.Color))

fit!(o, zip(t.Cut, t.Color))

plot(o, legendtitle="Color", xlabel="Cut")

HeatMap

o = HeatMap(-5:.1:5, -0:.1:10)

x, y = randn(10^6), 5 .+ randn(10^6)

fit!(o, zip(x, y))

plot(o)

plot(o, marginals=false, legend=true)

Naive Bayes Classifier

The NBClassifier type stores conditional histograms of the predictor variables, allowing you to plot approximate "group by" distributions:

# make data
x = randn(10^6, 5)
y = x * [1,3,5,7,9] .> 0

o = NBClassifier(5, Bool)  # 5 predictors with Boolean categories
fit!(o, zip(eachrow(x), y))
plot(o)